Beyond Federated Learning: Decentralized Agentic Systems for Self-Evolving Clinical Intelligence
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arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modelin...
The paper introduces MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self‑learn clinical knowledge through a multi‑agent pipeline of summarization, refinement, and application. MACD is extended into a human‑AI collaborative workflow where multiple diagnostician agents consult iteratively, guided by a judge agent and human oversight. Evaluation on the MIMIC‑MACD cohort shows significant gains in diagnostic accuracy—an average 11.6 percentage‑point improvement over authoritative knowledge for open‑weight LLMs and an 18.3‑percentage‑point boost over physician‑only diagnosis in text‑only vignettes.
arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strate...
arXiv:2608. 11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users.
CoMMa is a decentralized large language model (LLM) agent framework designed for oncology decision support. It allows specialists to work on partitioned clinical data streams, enforcing data decentralization and enhancing role specialization through agent-specific fine‑tuning. The framework introduces a contribution‑aware aggregation mechanism that replaces stochastic reasoning with deterministic embedding projections, enabling explicit credit assignment and a stable, interpretable decision pathway.
The paper evaluates the cost‑effectiveness of consensus‑based learning (CBL) versus federated learning (FL) across seven medical datasets, three tasks, and eight modalities involving 3 to 23 clients. CBL achieves accuracy comparable to FL while dramatically cutting training time (15‑fold) and communication cost (60‑fold). The study suggests that CBL offers a more sustainable and democratized approach to deploying collaborative AI in real‑world healthcare settings.